Machine Learning Classification based on image recognition for study of face symptoms in patients with Down's syndrome
| Autor: | Yi-min Su, 蘇逸民 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 In recent years, both facial recognition system and machine learning are developed rapidly. In these two areas, each has many commonly used methods. In this work, we use a two-stage approach on images for classification. First, we transform the facial image data into multi-dimensional data form, and then apply appropriate multivariate analysis and data mining methods on it. Methodologies such as Multilinear principal component analysis (MPCA), Regularized Discriminant Analysis (RDA), Random Forest are adopted. We aim at finding factors that are important in identifying different types of images. Although facial features are different for individuals, but most people with Down''s syndrome can be discriminated from the outlooks whether he or she has the disease or not. These variations sometimes can be seen from their facial features. For example, the features such as facial proportion, ear appearance, nose shape and eye contour and so on, can help to determine if a person has the disease or not. This thesis discusses how to use multivariate analysis and machine learning methods based on the facial image data to identify Down''s syndrome patients. According to some of the sample images from those with Down''s syndrome or not, we find the important areas on the facial image which are useful in discriminating Down''s syndrome patients. In the end, after restoring the important areas of the facial image, it is expected that the above methodology is helpful as one of the main criteria for the doctors to identify the Down’s syndrome symptoms with high accuracies. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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